Adaptive Reinforcement Learning in Dynamic Environments

Abstract

Reinforcement Learning (RL) has emerged as a powerful framework for sequential decision-making problems where agents learn optimal policies through interactions with an environment. However, traditional RL algorithms often assume stationary environments, limiting their effectiveness in real-world dynamic settings where the environment’s states, reward functions, or transition dynamics can change over time. Adaptive Reinforcement Learning (ARL) seeks to overcome this limitation by enabling agents to dynamically adjust their learning strategies in response to environmental changes. This paper presents a comprehensive review of ARL techniques tailored for dynamic environments, focusing on their ability to balance exploration and exploitation under non-stationarity. Various methods such as meta-learning, multi-armed bandits, policy adaptation, and transfer learning are explored to enhance adaptability and robustness. The research methodology involves systematic analysis of state-of-the-art algorithms applied in domains including robotics, autonomous driving, and financial decision-making. Key findings indicate that ARL approaches significantly improve learning efficiency and performance stability in non-stationary settings compared to classical RL. The workflow of ARL systems involves environment monitoring, change detection, adaptive model updates, and continual learning. Advantages include improved flexibility, faster convergence to new optimal policies, and resilience to environmental shifts, while challenges encompass computational complexity, model selection, and catastrophic forgetting. The discussion highlights promising trends such as hierarchical reinforcement learning and context-aware adaptation. The conclusion underscores the necessity of ARL for practical deployment of RL in dynamic real-world applications and calls for future research into scalable, sample-efficient, and explainable adaptive RL frameworks.